The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
The technology disclosed relates to motion capture and gesture recognition. In particular, it calculates the exerted force implied by a human hand motion and applies the equivalent through a robotic arm to a target object. In one implementation, this is achieved by tracking the motion and contact of the human hand and generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool.
The human hand is complex entity capable of both gross grasp and fine motor skills. Despite many successful high-level skeletal control techniques, modelling realistic hand motion remains tedious and challenging. It has been a formidable challenge to emulate and articulate the complex and expressive form, function, and communication of the human hand.
In addition, robotics is evolving rapidly, and its applications in the industry is also increasing from object pick and place robots, to move and locate robots. In fact, the field of robotics is moving so quickly, that the field encompasses a wider range of disciplines and applications than taught by traditional robotics education; which must adapt and incorporate a more multidisciplinary approach. One discipline that needs greater inclusion in robotics includes improved robot communication and interaction.
Existing gesture recognition techniques utilize conventional motion capture approaches that rely on markers or sensors worn by the occupant while executing activities and/or on the strategic placement of numerous bulky and/or complex equipment in specialized smart home environments to capture occupant movements. Unfortunately, such systems tend to be expensive to construct. In addition, markers or sensors worn by the occupant can be cumbersome and interfere with the occupant's natural movement. Further, systems involving large numbers of cameras tend not to operate in real time, due to the volume of data that needs to be analyzed and correlated. Such considerations have limited the deployment and use of motion capture technology.
Consequently, there is a need for improved techniques to capture the motion of objects in real time without attaching sensors or markers thereto and to facilitate recognition of dynamic gestures for robotics applications.